Generating Synthetic Training Data for Deep Learning-based UAV Trajectory Prediction

Stefan Becker, Ronny Hug, Wolfgang Huebner, Michael Arens, Brendan Morris

2021

Abstract

Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are increasingly replacing classic approaches in object tracking applications for motion prediction. On the one hand, these models can capture complex object dynamics with less modeling required, but on the other hand, they depend on a large amount of training data for parameter tuning. Towards this end, we present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. Since UAVs, or rather quadrotors are dynamical systems, they can not follow arbitrary trajectories. With the prerequisite that UAV trajectories fulfill a smoothness criterion corresponding to a minimal change of higher-order motion, methods for planning aggressive quadrotors flights can be utilized to generate optimal trajectories through a sequence of 3D waypoints. By projecting these maneuver trajectories, which are suitable for controlling quadrotors, to image space, a versatile trajectory data set is realized. To demonstrate the applicability of the synthetic trajectory data, we show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset. The evaluation is done on the publicly available ANTI-UAV dataset.

Paper Citation

in Harvard Style

Becker S., Hug R., Huebner W., Arens M. and Morris B. (2021). Generating Synthetic Training Data for Deep Learning-based UAV Trajectory Prediction. In Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS, ISBN 978-989-758-537-1, pages 13-21. DOI: 10.5220/0010621400003061

in Bibtex Style

@conference{robovis21,
author={Stefan Becker and Ronny Hug and Wolfgang Huebner and Michael Arens and Brendan Morris},
title={Generating Synthetic Training Data for Deep Learning-based UAV Trajectory Prediction},
booktitle={Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,},
year={2021},
pages={13-21},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010621400003061},
isbn={978-989-758-537-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,
TI - Generating Synthetic Training Data for Deep Learning-based UAV Trajectory Prediction
SN - 978-989-758-537-1
AU - Becker S.
AU - Hug R.
AU - Huebner W.
AU - Arens M.
AU - Morris B.
PY - 2021
SP - 13
EP - 21
DO - 10.5220/0010621400003061